Open AccessA Data Adaptive Model for Retail Sales of Electricity
Author(s) -
Johanna Marcelia
Publication year - 2021
Language(s) - English
Resource type - Dissertations/theses
DOI - 10.18122/td.1816.boisestate
Subject(s) - data set , process (computing) , electricity , set (abstract data type) , computer science , function (biology) , econometrics , data mining , engineering , economics , artificial intelligence , electrical engineering , programming language , operating system , evolutionary biology , biology
When fitting a model to a data set, the goal is to create a model that captures the trends present in the data. However, data often contains regions where the underlying model changes or exhibits shifts in certain parameters due to economic events. These locations in the data are known as changepoints, and ignoring them can result in high error and incorrect forecasts. By developing a specific cost function and optimizing using the genetic algorithm, we are able to locate and account for the changepoints in a given data set. We specifically apply this process to the retail sales of electricity in the United States by examining data sets from each state's residential, commercial, and industrial sectors. We demonstrate that, when changepoints are accounted for, model trends can be computed more accurately. We specifically explore this in the case of data sets that exhibit changepoints due to the 2020 (and ongoing) pandemic.